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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: December 2025
Volume 10 | Issue 12
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 10
Issue Published : 115
Article Submitted : 19456
Article Published : 8041
Total Authors : 21252
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| Paper Title: | Advanced Machine Learning-Based Detection of Security Anomalies in Smart Home Networks Using Deep Learning Techniques |
| Authors Name: | Shubi khajuria , Er. Ramandeep Kaur |
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IJRTI_205904
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| Published Paper Id: | IJRTI2508117 |
| Published In: | Volume 10 Issue 8, August-2025 |
| DOI: | |
| Abstract: | The rapid growth of smart home devices has introduced significant convenience but also expanded the attack surface for cyber threats. Traditional signature-based intrusion detection methods often fail to detect novel or evolving attacks targeting these IoT environments. This research investigates the use of advanced machine learning techniques—specifically deep learning models such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, alongside K-Nearest Neighbors (KNN)—to detect security anomalies in smart home networks. A simulated smart home environment was created to capture both benign and malicious network traffic, including SSH brute-force and botnet attacks. Data preprocessing involved feature extraction and dimensionality reduction techniques to enhance model training. Experimental results demonstrate that LSTM achieves the highest detection accuracy of 98.3% with the lowest false positive rate, effectively capturing temporal patterns in attack behavior. CNN also performs robustly in identifying spatial traffic anomalies, while KNN shows limitations with dynamic datasets. These findings suggest that integrating deep learning models into smart home security systems can significantly improve the detection and mitigation of sophisticated cyber threats. |
| Keywords: | Artificial Intelligence, Machine Learning, cybersecurity, accuracy,VAPT,SSH Brute Force Attacks |
| Cite Article: | "Advanced Machine Learning-Based Detection of Security Anomalies in Smart Home Networks Using Deep Learning Techniques", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 8, page no.b131-b133, August-2025, Available :http://www.ijrti.org/papers/IJRTI2508117.pdf |
| Downloads: | 000618 |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2508117
Registration ID:205904
Published In: Volume 10 Issue 8, August-2025
DOI (Digital Object Identifier):
Page No: b131-b133 Country: udhampur, Jammu and Kashmir, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2508117 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2508117 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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